title: Batch statistical process control of a fluid bed granulation process using in-line spatial filter velocimetry and product temperature measurements.
abstract: Fluid bed granulation is a batch process, which is characterized by the processing of raw materials for a predefined period of time, consisting of a fixed spraying phase and a subsequent drying period. The present study shows the multivariate statistical modeling and control of a fluid bed granulation process based on in-line particle size distribution (PSD) measurements (using spatial filter velocimetry) combined with continuous product temperature registration using a partial least squares (PLS) approach. Via the continuous in-line monitoring of the PSD and product temperature during granulation of various reference batches, a statistical batch model was developed allowing the real-time evaluation and acceptance or rejection of future batches. Continuously monitored PSD and product temperature process data of 10 reference batches (X-data) were used to develop a reference batch PLS model, regressing the X-data versus the batch process time (Y-data). Two PLS components captured 98.8% of the variation in the X-data block. Score control charts in which the average batch trajectory and upper and lower control limits are displayed were developed. Next, these control charts were used to monitor 4 new test batches in real-time and to immediately detect any deviations from the expected batch trajectory. By real-time evaluation of new batches using the developed control charts and by computation of contribution plots of deviating process behavior at a certain time point, batch losses or reprocessing can be prevented. Immediately after batch completion, all PSD and product temperature information (i.e., a batch progress fingerprint) was used to estimate some granule properties (density and flowability) at an early stage, which can improve batch release time. Individual PLS models relating the computed scores (X) of the reference PLS model (based on the 10 reference batches) and the density, respectively, flowabililty as Y-matrix, were developed. The scores of the 4 test batches were used to examine the predictive ability of the model.
authors.affiliation: Laboratory of Pharmaceutical Process Analytical Technology, Department of Pharmaceutical Analysis, Ghent University, Harelbekestraat 72, B-9000 Ghent, Belgium. anneleen.burggraeve@ugent.be
authors.forename: A
authors.initials: A
authors.lastname: Burggraeve
authors.forename: T
authors.initials: T
authors.lastname: Van den Kerkhof
authors.forename: M
authors.initials: M
authors.lastname: Hellings
authors.forename: J P
authors.initials: JP
authors.lastname: Remon
authors.forename: C
authors.initials: C
authors.lastname: Vervaet
authors.forename: T
authors.initials: T
authors.lastname: De Beer
mesh_terms: D002626:Chemistry, Pharmaceutical; D005947:Glucose / Q000737:chemistry; D016018:Least-Squares Analysis; D008956:Models, Chemical; D015233:Models, Statistical; D010316:Particle Size; D011208:Powders; D011786:Quality Control; D012212:Rheology / Q000295:instrumentation*; D013213:Starch / Q000737:chemistry; D013678:Technology, Pharmaceutical / Q000295:instrumentation* / Q000592:standards / Q000706:statistics & numerical data; D013696:Temperature
chemical_list: D011208:Powders; D013213:Starch; D005947:Glucose